Joint hyperbolic and Euclidean geometry contrastive graph neural networks

• Main Authors: XU, Xiaoyu, PANG, Guansong, WU, Di, SHANG, Mingsheng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Graph neural networks; Hyperbolic embedding; Contrastive learning; Graph representation learning; Databases and Information Systems; Graphics and Human Computer Interfaces; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/7564; https://ink.library.smu.edu.sg/context/sis_research/article/8567/viewcontent/Joint_Hyperbolic_and_Euclidean_Geometry_Contrastive_Graph_Neural_Networks_revision_version.pdf
• Institution: Singapore Management University

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in a wide variety of analytical tasks. Current GNN approaches focus on learning representations in a Euclidean space, which are effective in capturing non-tree-like structural relations, but they fail to model complex relations in many real-world graphs, such as tree-like hierarchical graph structure. This paper instead proposes to learn representations in both Euclidean and hyperbolic spaces to model these two types of graph geometries. To this end, we introduce a novel approach - Joint hyperbolic and Euclidean geometry contrastive graph neural networks (JointGMC). JointGMC is enforced to learn multiple layer-wise optimal combinations of Euclidean and hyperbolic geometries to effectively encode diverse complex graph structures. Further, the performance of most GNNs relies heavily on the availability of large-scale manually labeled data. To mitigate this issue, JointGMC exploits proximitybased self-supervised information in different geometric spaces (i.e., Euclidean, hyperbolic, and Euclidean-hyperbolic interaction spaces) to regularize the (semi-) supervised graph learning. Extensive experimental results on eight real-world graph datasets show that JointGMC outperforms eight state-of-the-art GNN models in diverse graph mining tasks, including node classification, link prediction, and node clustering tasks, demonstrating JointGMC's superior graph representation ability. Code is available at https://github.com/chachatang/jointgmc. (c) 2022 Elsevier Inc. All rights reserved.